Supabase ranks higher at 42/100 vs Open-Sourced MCP Servers Directory at 22/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Open-Sourced MCP Servers Directory | Supabase |
|---|---|---|
| Type | Repository | MCP Server |
| UnfragileRank | 22/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality |
| 0 |
| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a centralized web-based directory (mcp.so) that aggregates MCP servers submitted by the community, organizing them by category and making them searchable through a Next.js frontend backed by Supabase. The system accepts both GitHub URLs and raw JSON metadata, parses project information through a dedicated parseProject() service function, and stores normalized data in a relational schema with projects and categories tables for efficient querying and filtering.
Unique: Combines GitHub URL parsing with Jina AI for automatic content extraction and OpenAI-based summarization to enrich server metadata without requiring manual curation, storing normalized data in Supabase for efficient multi-dimensional filtering across categories, tags, and full-text search
vs alternatives: Provides a unified, categorized discovery experience specifically for MCP servers rather than generic GitHub search, with automatic metadata enrichment and community voting/rating potential
Processes submitted GitHub URLs or JSON payloads through a multi-stage extraction pipeline: parseProject() validates and normalizes input data, Jina AI extracts structured content from repository README and documentation, and OpenAI generates concise summaries and categorization. The enriched metadata is persisted to Supabase with fields for description, tags, installation instructions, and usage examples, enabling consistent presentation across the directory.
Unique: Chains Jina AI for repository content extraction with OpenAI for semantic summarization and automatic categorization, eliminating manual metadata entry while maintaining data quality through a parseProject() service layer that validates and normalizes heterogeneous input formats
vs alternatives: Reduces submission friction compared to manual directory entries while maintaining higher metadata quality than simple GitHub README parsing alone, leveraging LLM-based summarization to generate human-readable descriptions automatically
Implements the MCP Directory frontend using Next.js pages (Landing, Project Detail, Categories) and reusable React components (Search, Markdown Renderer, etc.), with responsive CSS for mobile, tablet, and desktop viewports. The architecture uses Next.js server-side rendering (SSR) or static generation (SSG) for performance and SEO, with client-side React components for interactive features like search and filtering. The UI layer communicates with backend API routes for data fetching and submission.
Unique: Uses Next.js for server-side rendering and static generation to optimize SEO and performance, with reusable React components for search, filtering, and markdown rendering, enabling fast initial page loads and excellent Core Web Vitals scores
vs alternatives: Next.js provides built-in SSR/SSG and API routes, reducing infrastructure complexity compared to separate frontend and backend; React components enable code reuse and maintainability compared to template-based approaches
Implements data persistence using Supabase (PostgreSQL-based) with two primary tables: projects (storing MCP server metadata including name, description, repository URL, category, tags, installation instructions) and categories (defining the taxonomy for organizing servers). The schema includes proper indexing on frequently-queried fields (name, category, tags), foreign key relationships for referential integrity, and timestamp fields (created_at, updated_at) for tracking submission and modification times. The architecture supports full-text search through indexed text fields and enables efficient filtering and pagination.
Unique: Uses Supabase (managed PostgreSQL) for data persistence with native full-text search indexing and real-time capabilities, eliminating the need for separate search infrastructure while maintaining SQL query flexibility
vs alternatives: Supabase provides managed PostgreSQL with built-in authentication and real-time subscriptions, reducing operational overhead compared to self-hosted databases; trades some customization flexibility for managed service reliability
Exposes Next.js API routes that accept POST requests with either a GitHub repository URL or raw JSON project metadata, validates input through a Project model with TypeScript type checking, and persists submissions to Supabase after enrichment. The API layer implements saveProject() to handle database writes, with support for both creation and updates, and includes error handling for invalid URLs, missing required fields, and API failures during enrichment.
Unique: Implements dual-input submission (GitHub URL or JSON) with automatic enrichment pipeline triggered server-side, using TypeScript Project model for compile-time type safety and Supabase for transactional persistence with automatic timestamp and ID generation
vs alternatives: Supports both URL-based and metadata-based submissions in a single API, reducing friction for developers while maintaining data consistency through server-side validation and enrichment rather than client-side responsibility
Implements search functionality through Next.js API endpoints that query the Supabase projects table using full-text search on server names, descriptions, and tags, combined with faceted filtering by category, tags, and other metadata fields. The frontend React components (Search component) provide UI for query input and filter selection, with results ranked by relevance and paginated for performance. The system maintains a denormalized schema with indexed text fields to enable fast queries across thousands of server entries.
Unique: Leverages Supabase's native full-text search capabilities with faceted filtering on pre-computed category and tag dimensions, providing fast keyword-based discovery without external search infrastructure like Elasticsearch
vs alternatives: Simpler to maintain than custom search implementations while providing adequate performance for community-scale directories; trades semantic understanding for operational simplicity and cost efficiency
Implements sitemap management APIs that dynamically generate XML sitemaps listing all MCP server project pages, with automatic updates triggered when new servers are submitted or existing ones are modified. The system maintains a sitemap index that references individual sitemaps (split by project count for Google compliance), with proper lastmod timestamps and priority values. Sitemaps are served at standard locations (/sitemap.xml, /sitemap-index.xml) for search engine crawlers to discover and index all directory content.
Unique: Dynamically generates sitemaps on-demand from Supabase project data with automatic splitting for Google compliance, integrated into the submission pipeline to ensure new servers are indexed immediately without manual sitemap updates
vs alternatives: Eliminates manual sitemap maintenance while ensuring search engines always have current project listings; dynamic generation trades some caching efficiency for guaranteed freshness
Maintains a categories table in Supabase that defines the taxonomy for organizing MCP servers (e.g., 'Data Access', 'API Integration', 'Development Tools'), with support for hierarchical relationships and metadata like descriptions and icons. The system enforces referential integrity between projects and categories, allowing servers to be tagged with one or more categories. The frontend Categories page displays all available categories with server counts, enabling users to browse by functional area rather than keyword search.
Unique: Implements category taxonomy as a first-class Supabase table with referential integrity, enabling both UI-driven browsing and programmatic filtering while maintaining data consistency through foreign key constraints
vs alternatives: Provides structured categorization superior to free-form tagging alone, with enforced consistency and server counts per category; simpler than hierarchical taxonomies but sufficient for most MCP server use cases
+4 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Open-Sourced MCP Servers Directory at 22/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities